Description

Unsupervised learning is one of the main streams of machine learning,
and closely related to exploratory data analysis and data mining. This
course describes some of the main methods in unsupervised learning.

In recent years, machine learning has become heavily dependent on
statistical theory which is why this course is somewhere on the
borderline between statistics and computer science. Emphasis is put
both on the statistical formulation of the methods
as well as on their computational implementation. The goal is not
only to introduce the methods on a theoretical level but also to show
how they can be implemented in scientific computing environments
such as Matlab or R. Computer projects are an important part of the
course.

How to obtain the credits

There are two ways of getting credits for this course:

Taking the exam which consists of solving mathematical problems. The exam is on Mon 4th May in B123 & CK112, at 16:00-19:00 o'clock.

Doing computer projects which consist of programming in either Matlab or R (you can choose which one you use)

If you do one of these, you get 4 cu. If you do both of them, you get 6 cu. You are strongly encouraged to do both of them.

You also have the option of handing in the mathematical exercices given in the exercice sessions. This will give you extra points worth, at the maximum, 20% of the total points. Details

Computer science majors: Bachelor's degree recommended. It should include the mathematics courses listed above for mathematic majors, as well as
both the courses "Introduction to machine learning" and "Probabilistic models" or their previously lectured counterpart "Computational data analysis I"

Course material

There is no book for the course. Handouts, typically chapters of books, will be
provided, and together with some lecture slides, these will contain
the material of the lectures. This material, together with the exercices, will be made available here (material will be added after each lecture). You will need a login name and password which are given during the lectures (or you can email the assistants)